Data is Meaningless (Not Really – Hear Me Out)

As the new Daxko Product Manager focused on all things reporting and analytics across Daxko products, I spend 100% of my time listening to users, documenting needs, and forming and managing plans to ensure that what we do now will improve what is delivered to customers in the short and long terms. This is sincerely fun stuff.

In my 13 years of working with data in different jobs — from U.S. Space Command (yep, you can ask me about satellites) to federal child welfare benchmarking (happy to chat about child well-being trends) to Y-USA and Y data (program, membership and impact – you name it!), my hands-down favorite thing is this…

Data doesn’t answer questions well.

Nope, I’m not kidding.

Intuitively, we all know this. If your blood pressure is 160 over 110, you think, “that’s not good” … BUT if it was 170 over 120 when you measured it two weeks ago, then all of a sudden those very same numbers make you think, “I’m improving! This is good (or at least better)”. Those “bad looking” numbers are telling you you’re moving in the right direction.

As you might infer, “how was that collected?”, “so what?”, “compared to what?” and “well, that depends … “ are my go-to responses. That’s because if we don’t answer these questions we are in danger of not understanding what is actually going on and making bad decisions as a result.

Data need to be many things to be meaningful. At a minimum, it needs the following:

#1: It needs to be correct. A nurse measuring your blood pressure needs to not only know how to use the exact model of the cuff they put on your arm, they also need to read, remember, and write-down the right two numbers while using it. To go one further, the doctor who reads what the nurse wrote has to be able to decipher his or her handwriting or the whole process is nullified. The industry word for this is data integrity. It’s self-explanatory why it’s vital, but it’s also very easily not achieved — or even realized if you don’t have it.

#2: It needs to be presented in context. This is how we know what’s good, bad, or trending in a certain direction. In this example, we have blood pressure guidelines for healthy ranges. Not only do those exist, but there are different ranges for how old you are, if you’re male or female, or if you’re on a plan with your doctor to reach a certain goal. Heck, they can even change over time as new research emerges. Good health care providers will also tell you to consider this information in combination with other factors, such as family history, diet, weight, etc.

#3: It needs to be digestible. You could have access to the best information in the world, but if you can’t explain what you have, you can’t use it. If we all needed to explain systolic (top number) and diastolic (bottom number) of blood pressure, many would feel overwhelmed or get stuck on information that doesn’t answer their questions. In this example, if #1 and #2 above are done well, it’s much easier to take-in your blood pressure ratio than also having to take-in all the research behind it.

I’m drawn to analysis and reporting because good data is different than available data. I would argue that meaningful data is more important than Big Data, we’ve-always-collected-that data, and that’s-interesting data.

So let’s revise what’s above…

Data collected via sound methodologies and presented in appropriate context in a way that can be understood answers questions VERY well.

You can reach me at cmiller@daxko.com if you have thoughts about Daxko data and reporting – I’d love to talk to you.

Constance M. is a Product Manager who enjoys organizing stuff and all things Joss Whedon.